Backtrader Template

Backtrader Template creates new environments for onboarding clients at RunBacktest. The benefits of working with Backtrader Template are:

• Easy management of parameters in a setup file.
• Easy ability to run multiple test for optimization using multi-processing.
• Simple to change outputs to terminal.
• Easy integration of analyzers and indicators.
• Standard outputs available:
  • Terminal
  • Excel spreadsheets
  • Database
  • Tearsheets
• Single backtest details analysis in Jupyter.
• Multi-backtest analysis in Jupyter.

Installation

Follow these steps to install and run a basic backtest.

1. Clone the repo to your local machine.
   • HTTPS: git clone https://github.com/neilsmurphy/backtrader_template. git Then enter your user name and password.
   • SSH: [email protected]:neilsmurphy/backtrader_template.git
2. Create a virtual environment in the main repo directory.

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   

3. Run a backtest with standard settings.

   python3 setup.py
   

Setup

The key file for interfacing with Backtrader Template is the setup.py file. Here you will set your parameters for your backtests.

The first section of the backtest contains:

• print_params: This will print out all the parameters that will be used in the backtests, including those that are defaults and not visible. Additionally shows the lists of inputs for each parameter if it exists.
• run_tests_now: Allows turning on/off the actual running of the backtests. Useful if you just want to view the parameters before running tests.
• multi_pro: Determines if the multi-processor gets used. True to use multi-processing, false to run each backtest one at a time.
• reset_database: Used to delete all data in the database before running a backtest. If True, all the tables will be deleted from the database provide a clean slate for the backtest results. You will be prompted to confirm the deletion. If false, the data in the database is left as-is and the backtest result can be added to the database which allows for access to the current and older backtests.

Individual test parameters.

Running backtests

All stock data is downloaded using yahoo finance. Time frames are daily.

A simple indicator is used in this backtests. Of course this would be replaced with the users indicators.

When setting dates, the from_date is when data is loaded, and trade_start is when trading starts. While Backtrader will automatically adjust lead times, this gives more control of the actual trade start date.

print_dev is used for custom logging. You can change this in extension/strategy.py.

Multiple backtest can be run for optimization. All that is required is to use list where your parameters would be.

For example, run the following tests:

...
instrument= ["FB", "TSLA", "AAPL"],
benchmark="SPY",
sma_fast=range(15, 46, 15),
sma_slow=range(30, 61, 15),
limit_price=[0.04, 0.07, 0.09],
stop_price=[0.02, 0.05],
...

This will run the following tests.

There will be 108 backtests run.

    batchname="Single Test",
    batch_runtime="2021-06-20 06:52",
    db_name=None,
    test_number=0,
    save_result=True,
    save_tearsheet=True,
    save_excel=True,
    save_db=False,
    full_export=True,
    save_path="result",
    save_name="my test name",
    from_date="2016-01-01",
    trade_start="2016-09-01",
    to_date="2020-12-31",
    duration=None,
    instrument=['FB', 'TSLA', 'AAPL'],
    benchmark="SPY",
    initinvestment=100000,
    commission=0.0,
    margin=None,
    mult=1,
    print_dev=False,
    print_orders_trades=True,
    printon=True,
    print_ohlcv=-1,
    print_final_output=True,
    ploton=False,
    sma_fast=range(15, 46, 15),
    sma_slow=range(30, 61, 15),
    limit_price=[0.04, 0.07, 0.09],
    stop_price=[0.02, 0.05],
    trade_size=1.0,

The system will automatically calculate all of the possible combinations. The output will tell you how many test will run. If you want to just check how many test will run, set run_test_now = False and run the backtests. It won't actually run, just give you the parameters and number of test that will execute. Criteria is set in the calculation of the backtest to ensure that the long sma is higher than the short sma. This logic can be found in main/RunBacktest/scenario at line 457.

if scenario["sma_fast"] >= scenario["sma_slow"]:
    continue

When running multiple backtest, make sure to use multi-pro = True to turn on the multi-processor. The multi-processor is set to use your number of cores - 2.

To run multiple test at once over different dates, use the from_date combined with duration. trade_start and to_date are ignored.

Backtest results

View your backtest results on the terminal, save to excel, database, or create a tearsheet.

Terminal

There are several options for diplaying backtest results to the terminal. To view all orders and trades, print_orders_trades. One can also select to view all of the ohlcv candles using print_ohlcv=0. If there are more than one data lines in the backtest, select increasing increments, eg: print_ohlcv=1 to find the desired dataline. Use -1 for off. Backtrader template offers a trade output list which can be accessed via print_final_output=True [Thanks to https://github.com/ab-trader] There are some sundry printouts such as the final value, etc. printon=True Also is available the ability to create a custom log to terminal using 'print_dev=False,' This can be modified in extensions/strategy.py.

To disk

There are three options for saving to disk. To turn on/off saving in general, use save_result=False. To set the path for saving, save_path="result" This will save to the "result" directory. It is not necessary to create the directory, it will be made if not already existing. A custom name can be added to the output file names. save_name="my test name". To save to excel, use save_excel=True. The spreadsheet will have multple tabs with the following information:

• trade_list: Summary of all trades.
• trade_analysis: Backtraders detailed backtest TradeAnalysis.
• drawdown: Overall drawdown stats.
• transaction: Each transaction, including partial fills.
• value: Cash and market value at each bar.
• trade: Backtrader's trade summary.
• ohlcv: Stock OHLCV bars.
• benchmark: Benchmark OHLCV bars.
• dimension: These are the input parameters and other settings for the backtest.

The above data can also be saved to database using save_db=True This is necessary for analysis. This default template uses SQLite3 for simplicity, but any database could be used. I personally use postgres.

There is a very nice tearsheet provided by QuantStats. This can be accessed by using save_tearsheet=True. Here is a sample:

Memory

A discussion about memory. I try to separate analyzers into two categories. Those that essentially have one line output per test, and analyzers that have multiple lines per test. If I'm only executing a modest number of backtests, I will go ahead and use a 'full_export' to the database, meaning all analyzers. But saving OHLV data and other such large datasets is not conducive to large numbers of backtests. For large backtests I will set 'full_export=False' which is good for fast backtesting. You can control which analyzers are included in full or not full in the extension/analyzer module at the bottom.

Analysis

There are two analysis notebooks.

1. single_analysis.ipynb Used to provide a detailed analysis for a single backtest. The charts are the same as contained in the tearsheet.
2. analysis.ipynb Used for comparing multiple spreadsheets across parameters primarily using heatmaps.

Create new parameters

To add a new parameter to the backtest, just add it into the RunBacktest class self.params dictionary found in main.py. The default for the parameter is placed in the first position of the list. This will then become available in the strat as a parameter. For example, adding in rsi as a true/false parameter:

rsi=[True, True],

This parameter is now availabe in your backtest in the strategy by using

self.p.rsi

The second parameter is boolean and is used to determine if this parameter is exported to the database after running the backtest for use in analysis.

Extensions

Separate modules are stored in the extension directory and then import to main as needed. Modules that are stored here are:

• analyzer: For gathering information on test results.
• indicator: Creates signals for trading.
• result: For generating spreadsheets and database outputs.
• sizer: Can be used for sizing trades. (Not used in default settings.)
• strategy: Superclass for strategy with standard methods.

CCXT and crypto currencies

Now encorporated is Dave Valance/Ed Bartosh ccxt stores into backtrader. You can now backtest cryptos on a wide variety of exchanges. Once backtested, you can sandbox test your algo, then go live.

The sample included is Binance. Set up your parameters as per the params-template.json file.

I have set up two apis, one for actual trading and one for the testnet sandbox.

 "binance_actual": {
    "apikey": "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~",
    "secret": "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~",
    "testorders": true
  },

  "binance_testnet": {
    "apikey": "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~",
    "secret": "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~",
    "testorders": true
  },

Create your configs for your store. For the testnet use:

config = {'urls': {'api': 'https://testnet.binance.vision/api'},
          "apiKey": params["binance_testnet"]["apikey"],
          "secret": params["binance_testnet"]["secret"],
          "enableRateLimit": True,
          }

For your actual store with live trading, use:

config = {
    "apiKey": params["binance_actual"]["apikey"],
    "secret": params["binance_actual"]["secret"],
    "enableRateLimit": True,
}

When trading on the sandbox, set sandbox=True when creating the store. And sandbox=False when trading live.

store = CCXTStore(
    exchange="binance", currency="USDT", config=config, retries=5, debug=False, sandbox=True
)

Dependencies

Packages required:
Black
Dash Bootstrap Components
Jupyter
Quandl
Pandas
Plot.ly
Plot.ly Dasy
tabulate
urllib3
XlsxWriter
Yfinance

License

MIT

Contact

If you wish to contact me, I can be reached:

Neil Murphy
[email protected]
RunBacktest